STATDSPHD - Statistics and Data Science
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Core Major Units: 38
Additional Units: 24 (see requirements below for students with or without emphases)
Required Minor Units: 9
Total Minimum Degree Units: 71
Core Coursework: 38 units
20 units from the following Statistics courses:
course / course: Advanced Statistical Regression Analysis (3)
course: Statistical Computing (3)
course: Scientific Writing, Presentation and Bioethics (2) OR course: Scientific Grantsmanship (2)
18 units of Dissertation:
course: Dissertation (18)
For students with no emphasis, take the following coursework.
12 units of Statistics courses:
Students with no emphasis select a minimum 12 units from any of the following.
course: Statistical Genetics for Quantitative Measures (3)
course: Biostatistics for Research (3)
course: Applied Biostatistics Analysis (3)
course: Data Management and the SAS Programming Language (3)
course: Analysis of Categorical Data (3) OR course / course: Categorical Data Analysis (3)
course: High Dimensional Health Data Analysis and Machine Learning (3)
course: Clinical Trials and Intervention Studies (3)
course: General Linear and Mixed Effects Models (3)
course: Special Topics in Biostatistics (3) - needs prior approval; depends on topic
course: Survival Analysis (3)
course: Biostatistics Seminar (1)
course: Detection and Estimation in Engineering Systems (3)
course: Spatio-Temporal Ecology (3)
course: Introduction to Econometrics (3)
course: Econometrics (3) OR course: Advanced Applied Econometrics (4)
course: Econometrics (3)
course: Statistical Package for Research (3)
course: Educational Tests and Measurements (3) OR course: Statistical Methods in Psychological Research (3)
course: Multivariate Methods in Educational Research (3)
course: Theory of Measurement (3)
course: Theory of Measurement (3)
course: Advanced Data Analysis: Structural Equation Modeling (3)
course: Advanced Data Analysis: Dyadic Data and Bivariate Systems (3)
course: Advanced Data Analysis: Multilevel Modeling (3)
course / course / course: Spatial Statistics and Spatial Econometrics (3)
course: Applied Time Series Analysis (1-3)
course: Statistical Natural Language Processing (3)
course: Advanced Statistical Natural Language Processing (3)
course: Topics in Modern Analysis (3)
course: Theory of Graphs and Networks (3)
course: Stochastic Processes (3)
course: Stochastic Processes (3)
course: Stochastic Differential Equations (3)
course: Applied Stochastic Processes (3) OR course / course: Stochastic Methods in Surface Hydrology (3)
course: Statistical Machine Learning (3)
course: Bioinformatics and Functional Genomic Analysis (3)
course: Multivariate Analysis in Management (3)
course: Adaptive Optics and Imaging Through Random Media (3)
course: Principles of Image Science (3)
course: Statistical Mechanics (3)
course: Research Design & Analysis of Variance (3)
course: Graphical Exploratory Data Analysis (3)
course / course: Advanced Geographic Information Systems (3)
course: Stochastic Modeling I (3)
course: Engineering Decision Making Under Uncertainty (3)
course: Queuing Theory (3)
course: Simulation Modeling and Analysis (3)
course: Fundamentals of Optimization (3)
course: Advanced Quality Engineering (3)
course: Social Statistics (3)
course: Bayesian Statistical Theory and Applications (same as course) (3)
course: Survey Sampling (3)
course: Independent Study (1-6)
course: Research (1-3)
All Statistics & Data Science Graduate Students are required to complete Communications requirements.
Prepare a basic web page containing information on their own research, teaching, and other professional activities and make this page available through the Program’s web site.
Prepare a professional CV and post it on the web site.
Write articles or proposals and give lectures or presentations for audiences of various levels of sophistication. At least one of these activities must be verbal, and at least one must be written. For a complete list of activities see the Student Handbook.
Students must pass the Statistics & Data Science Qualifying Exam.
A minimum of 9 units for the Ph.D. minor.
Graduate College requirements stipulate that a minimum of 9 units be applied for the Ph.D. minor. Minor requirements are fixed by the minor department or program; some Minor programs require upwards or 12 or even 15 units for completion. (A Ph.D. Minor in Statistics cannot be counted towards a Ph.D. in Statistics.) The selection of the Ph.D. Minor field is to be made by the student in consultation with her/his advisor and the Program director. The Minor should reflect the student’s transdisciplinary interests, and wherever possible should be coordinated with the student’s additional Statistics electives.
Please refer to the Graduate Student Handbook for students who are pursuing this program of study.
Minimum Emphasis Units
24 (in addition to Core Major Units and Minor Units above)
Emphasis Core Coursework Requirements
3 units of Statistical Informatics coursework:
course: Statistical Computing (3)
Emphasis Elective Coursework
Additional Elective Courses; minimum 21 units
Minimum 6 units from Theme (a) "General", with minimum 6 units from any other single "theme" (b)–(g), and any 9 additional units from the list below (no course can be used if it overlaps with a course required by the minor, above). It is the student’s responsibility, prior to enrolling in any of the electives listed below, to complete any courses listed as prerequisites by the offering unit.
(a) General
course: High-Dimensional Health Data Analysis and Machine Learning (3)
course: General Linear and Mixed Effects Models (3)
course: Bayesian Statistical Theory and Applications (same as course) (3)
course: Topics in Modern Analysis (3)
course: Stochastic Modeling I (3)
course: Fundamentals of Optimization (3)
(b) Bioinformatics
course: Functional and Evolutionary Genomics (4)
course: Algorithms in Bioinformatics (3)
course: Informatics in Biology (3)
course: Bioinformatics and Functional Genomic Analysis (3)
(c) Business & management informatics
(d) Computing
course: Data Management and the SAS Programming Language (3)
course: Statistical Package for Research (3)
course: Artificial Intelligence for Health and Medicine (3)
course: Probabilistic Graphical Models (3)
course: Bioinformatics and Functional Genomic Analysis (3)
(e) Geographic information systems (GIS)
course: Integrated Geographic Information Systems (3)
course / course: Advanced Geographic Information Systems (3)
(f) Medical informatics
course: Healthcare Informatics: Theory and Practice (3)
course: Clinical Trials and Intervention Studies (3)
course: Pharmaceutical Calculations (1)
(g) Specialized theme
Where needed to suit a particular or specialized need in an individual student’s program of study, petition may be made to the GIDP Executive Committee for approval of an alternate, tailored 6-15 unit Theme. The student must be in good standing and must exhibit ongoing, satisfactory progress towards completion of the degree. The burden of proof for admitting a commensurate, Specialized Theme rests with the student, and the decision of the committee will be final.
Additional Emphasis Requirements
See additional requirements for overall plan above.
Minor Requirements for Doctoral Students in this Emphasis
See minor requirements for overall plan above.